Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2022 May:101:103693.
doi: 10.1016/j.apergo.2022.103693. Epub 2022 Feb 7.

Classifying hazardous movements and loads during manual materials handling using accelerometers and instrumented insoles

Mitja Trkov  1 , Duncan T Stevenson  2 , Andrew S Merryweather  3
Affiliations

Classifying hazardous movements and loads during manual materials handling using accelerometers and instrumented insoles

Mitja Trkov et al. Appl Ergon. 2022 May.

Abstract

Improper manual material handling (MMH) techniques are shown to lead to low back pain, the most common work-related musculoskeletal disorder. Due to the complex nature and variability of MMH and obtrusiveness and subjectiveness of existing hazard analysis methods, providing systematic, continuous, and automated risk assessment is challenging. We present a machine learning algorithm to detect and classify MMH tasks using minimally-intrusive instrumented insoles and chest-mounted accelerometers. Six participants performed standing, walking, lifting/lowering, carrying, side-to-side load transferring (i.e., 5.7 kg and 12.5 kg), and pushing/pulling. Lifting and carrying loads as well as hazardous behaviors (i.e., stooping, overextending and jerky lifting) were detected with 85.3%/81.5% average accuracies with/without chest accelerometer. The proposed system allows for continuous exposure assessment during MMH and provides objective data for use with analytical risk assessment models that can be used to increase workplace safety through exposure estimation.

Keywords: Activity classification; Lifting load and frequency estimation; Manual material handling.

PubMed Disclaimer

Conflict of interest statement

Declaration of interests

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Figures

Figure 1:
Figure 1:
Manual material handling tasks performed by the participants during experimental testing. (a) Lifting in a squat (left) and stoop (right) postures. (b) Overextended lifting (left) and lifting with jerky behavior (right). (c) Asymmetric lifting with twisting torso and stationary feet (left) and carrying the box with proper positioning in front of the load (right). (d) Lifting/lowering of the box from the table/knuckle height to the chest height from right to left side (left) and left to right side (right). (e) Pushing (left) and pulling (right) a cart. (f) Lifting a box from the ground (left) and from the table (right) and carrying it.
Figure 2:
Figure 2:
Distribution of data per activity type and weight.
Figure 3:
Figure 3:
Results of classification accuracies for all classifiers (i.e., SVM, KNN, Bagged Tree, Naive Bayes, and Single Tree) with and without considering load distinction.
Figure 4:
Figure 4:
Effect of the window size (i.e., buffer size) on the overall classification accuracy of SVM and KNN classifiers for data considering load distinction.
Figure 5:
Figure 5:
Confusion matrix results for SVM classifier on an independent test set. Cell values show row normalized values (TPR, FNR).
Figure 6:
Figure 6:
Continuous frequency computed from true activity as labeled manually (solid) and predicted activities filtered (dotted) for SVM classifier and the resulting RNLE Lifting Index.
See this image and copyright information in PMC

References

    1. Aly M, 2005. Survey on multiclass classification methods. Neural Networks 19, 1–9.
    1. Anjum A, Ilyas MU, 2013. Activity recognition using smartphone sensors. In: 2013 IEEE 10th Consumer Communications and Networking Conference (CCNC). IEEE, pp. 914–919.
    1. Antwi-Afari M, Li H, Yu Y, Kong L, 2018. Wearable insole pressure system for automated detection and classification of awkward working postures in construction workers. Automation in Construction 96, 433–441.
    1. Antwi-Afari MF, Li H, Seo J, Wong A, 2019. Automated recognition of construction workers’ activities for productivity measurement using wearable insole pressure system. In: Proceedings of the CIB World Building Congress 2019 - Constructing Smart Cities. International Council for Research and Innovation in Building and Construction (CIB). CIB, pp. 1–11.
    1. Antwi-Afari MF, Li H, Umer W, Yu Y, Xing X, 2020. Construction activity recognition and ergonomic risk assessment using a wearable insole pressure system. Journal of Construction Engineering and Management 146 (7), 04020077.